Prostate cancer (PC) is a malignant tumor that originates in the prostate gland it is the most commonly diagnosed and treated malignancy and is one of the leading causes of cancer-related death among males in western countries (1). About 1 in 6 men will be diagnosed with prostate cancer over the course of their life and most elderly men eventually develop the disease.
At the time of diagnosis, most men have localized prostate cancer (cancer confined to the prostate gland) with excellent prognosis. About 5% of men are diagnosed with advanced or distant cancer that has spread throughout the body. For these men, the 5-year relative survival rate is only 29%. Early detection of PC is however crucial since curative treatment only is possible for non-metastatic PC.
PC is typically diagnosed on the basis of increased serum prostate specific antigen (PSA) levels followed by histopathological inspection of needle biopsies. The use of PSA for PC detection, however, is associated with considerable false positive rates. Using a PSA cutoff of 4.0 ng/ml for screening, there is a 65% false-positive and a 20% false-negative rate (2). In particular, PSA levels in the intermediate range area (2-10 ng/mL) presents a gray zone area with very low predictive value. Naturally, such rates have spurred a search for other biomarkers, in particular biomarkers that are found in easy accessible bio-fluids and which are relatively cheap to assess. Until now, we are not aware that this endeavor has been successful.
Hence, there is a serious and unmet need to develop methods which can improve the early diagnosis of PC and reduce the number of men referred to needless biopsies. The present study presents one such method.
An emerging new class of potential biomarkers for prostate cancer is the microRNAs (miRs).
MicroRNAs comprise a class of endogenous small non-coding regulatory RNAs (˜22 nt), which control gene expression at the posttranscriptional level in diverse organisms, including mammals (3). MicroRNAs are transcribed as long imperfect paired stem-loop primary microRNA transcripts (pri-microRNAs) by RNA polymerase II, and further processed into hairpin precursor microRNAs (pre-microRNAs) by the nuclear RNase III endonuclease, Drosha (4). After export to the cytoplasm by Exportin-5-Ran-GTP, another RNase III endonuclease, Dicer, cleaves the pre-microRNA into a mature ˜22 nt microRNA duplex (4). Mature microRNAs mediate their function while incorporated in the microRNA-induced silencing complex (miRISC). The microRNA guides this complex to perfect/near perfect complementary target mRNAs, leading to either translational inhibition or mRNA degradation (5).
MicroRNAs are one of the most abundant classes of gene regulatory molecules and the latest release of the miRBase (version 21) contains 2588 mature human microRNAs (1881 precursors) http://www.mirbase.org/ (6). Together microRNAs have been estimated to regulate up to two thirds of all human mRNAs. Consequently, microRNAs influence numerous processes in the cell, for instance cell differentiation, cell cycle progression and apoptosis, and deregulation of microRNAs are often connected to human pathologies, including cancer (7). Additionally, some microRNAs appear to be cell type and disease specific and deregulated microRNA expression has been associated with both development and progression of cancer (8). Thus, aberrant microRNA expression has been investigated as a promising potential source of novel biomarkers for early cancer diagnosis (8). Moreover, microRNAs have potential to be used as targets of microRNA-based therapeutics for cancer (9). Several microRNA profiling studies have also reported aberrantly expressed microRNAs in the development and/or progression of PC (10). However, most of the microRNA biomarker studies in PC published to date have used relatively low patient sample numbers and often lack stringent independent clinical validation to confirm the biomarker potential of the identified microRNA candidates.
Importantly, to the best of our knowledge, no diagnostic method based on microRNA biomarkers detected in non-invasive samples such as urine has emerged.
Here we performed miRnome profiling of more than 750 of the most abundant microRNAs and selected the 183 microRNAs detectable in cell free urine across different disease stages (Example 1). From these 183 microRNAs we identified significantly aberrant regulated microRNAs in patients with benign prostate hyperplasia vs. PC patients where urine was collected prior to prostate removal by radical prostatectomy (RP) (Example 2). From this dataset we have identified a small group of miRs which are significantly different expressed in PC relative to non-PC subjects. We furthermore identified a diagnostic classifier consisting of only 2 microRNAs in cohort 1 and evaluated its diagnostic accuracy. This 2 microRNA classifier was successfully validated in an independent cohort 2 (Example 3). To investigate the robustness we applied the assay to yet an independent cohort (cohort 3) where specimens were sampled and processed with a considerably different methodology than cohort 1 and 2 (Example 4). Interestingly, the 2 microRNA classifier was also successfully validated in this cohort.
Further, as the discovery cohort suffered from being limited in the number of controls it appeared advantageous to merge cohort 1 and cohort 2 in order to build a classifier with a stronger statistic power based on the merged data. This approach was pursued in Example 5, and resulted in identification of a slightly different 20-miR classifier, but with a surprisingly high AUC of 0.99 with a specificity of 95.5% and a sensitivity of 93.6%.
The 2 miR diagnostic classifier (involving a ratio calculation) demonstrated improved accuracy compared to all single miRNAs tested. Interestingly, all the identified classifiers appeared to have an significantly improved accuracy compared with the total prostate specific antigen (tPSA) test. AUC of this test has been reported to be as low as 0.59, (11) or even lower (12).
To confirm the validity of the identified microRNA biomarkers we included an additional set of 205 PC samples and repeated the classifier building and validation in two new cohorts (cohort 4 and cohort 5, respectively), where the non-cancer samples from cohort 1 and 2 were distributed evenly and randomly between the two cohorts. Also, a different statistical method for assay selection and classifier building was applied, along with more stringent data filtering parameters.
From this study we identified diagnostic classifiers consisting of 3 to 10 microRNAs (with a significant overlap with the previous studies) and evaluated their diagnostic potential in cohort 4 (Example 8). The classifiers were successfully validated in an independent validation cohort 5 (example 9). Surprisingly, the classifiers were even more successful when validated in the intended use sub-population; patients with PSA levels below 10 ng/mL (Example 10).
As mentioned, ratio based classifiers are attractive due to their ability to circumvent the need for normalization assays, thereby reducing the number of assays included in the test. We identified two such ratio based classifiers consisting each of only three assays with high diagnostic potential in the discovery cohort 4 (example 11). The two ratio based classifiers were successfully validated in the validation cohort 5 (example 12). Finally, these ratio based classifiers were validated in the intended use sub population, where they proved to be even more accurate. Again, both the 3-10miR classifiers and the two ratio based classifiers demonstrated improved accuracy compared to all single miRNAs tested. Interestingly, all the identified classifiers appeared to have a significantly improved accuracy compared with the total prostate specific antigen (tPSA) test. As was the case for the 2 miR classifier of example 4, we also tested the diagnostic robustness of the two 3-miR ratio-based classifiers in the fundamentally different cohort 3. This analysis show that at least one of the 3-miR ratio-based classifiers is robust and validate even in cohort 3.